Methods and processes for utilizing information collected for enhanced verification

ABSTRACT

A system for verifying a user identity. The system comprises one or more memory devices storing instructions and one or more processors configured to execute the instructions. The processors are configured to receive information associated with an account of a user. The processors are further configured to generate a first profile, where the first profile being related to the user. The processors also receive an indication that the account is accessed by an accessor through an accessor device; and receive, from the accessor device, identity data comprising a plurality of data subsets associated with the accessor. The processors are configured to store the data subsets in respective clusters. The processors are further configured generate cluster analyses by analyzing the data subsets in respective clusters; and output the cluster analyses to node instances that weighs the cluster analyses outputs. The processors also generate a second profile, the second profile related to the accessor and being based on the received identity data and weighted cluster analysis. And the processors are configured to determine a likelihood factor that the accessor is the user based on a comparison of the first profile and the second profile.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/332,266, filed May 27, 2021, which is a continuation of U.S. patentapplication Ser. No. 15/930,149, filed May 12, 2020, which is acontinuation of U.S. patent application Ser. No. 16/191,013, filed Nov.14, 2018, which is a continuation of U.S. patent application Ser. No.15/972,697, filed May 7, 2018. The content of the foregoing applicationsis incorporated herein in its entirety by reference.

TECHNICAL FIELD

The present disclosure generally relates to a system for verifyingidentity for access to a user account.

BACKGROUND

Reliably and seamlessly verifying an accessor identity in grantingaccess to user accounts is a burdensome task for users.

In general, a system may utilize three factors in verifying a user. Thesystem may verify a user by (1) confirming accurate use of ausername/password, (2) confirming user account access by a known device,and (3) confirming user identity through enhanced data such as biometricdata. However, a user's own tendencies may limit the system'sverification process. For instance, the user may use a weak orcompromised username/password, because either it is leaked on theDarkweb, is the same username/password as every other account of theuser, or lacks significant encryption. Likewise, the user may havefailed to setup known devices, so that the system is unaware ofappropriate access by certain devices. Moreover, biometric verificationmay not be available given the device sensor capabilities. Additionally,users often find that use of any or all of these security procedures isso burdensome that access to the user's account (whether that account isprovided by a financial service provider, merchant, a socialapplication, etc.) becomes unacceptably inconvenient. The bottom line isthat enhanced security may prevent utilization of the user account forits intended purpose.

Moreover, while some solutions exist for verifying the identity of anentity attempting to access a user account, such solutions may beplagued by the difficulties addressed above. Also, such solutions areinefficient, and do not appropriately collect and utilize data. Forinstance, there exists a plethora of data available for an identityverification system that current systems do not currently utilize. Everyattempt by an accessor generates multiple data points, both active andpassive, both from the accessors themselves or from the accessingdevices. However, the majority of this data goes unused. For example, inorder to counter a user's lack of diligence in setting up known devices,a system could analyze passive device information from devices alreadysuccessfully accessing the user account, such as the device operatingsystem, model number, process chipsets, browser type, etc. Likewise, thesystem could utilize user information such as their active error rate,mouse speed, scroll speed, typing speed, eye movement rate, etc.Additionally, users also have static, or passive, data, such asbiometric (fingerprints, iris scans, palm vein pulse, heartbeat, etc.)data. All of this data could be implemented in a system to provide aseamless verification system that is constantly collecting data andconstantly analyzing the user's access to their account.

The present disclosure provides systems and devices to solve these andother problems.

SUMMARY

In the following description, certain aspects and embodiments of thepresent disclosure will become evident. It should be understood that thedisclosure, in its broadest sense, could be practiced without having oneor more features of these aspects and embodiments. Specifically, itshould also be understood that these aspects and embodiments are merelyexemplary. Moreover, although disclosed embodiments are discussed in thecontext of a processor bracket and, it is to be understood that thedisclosed embodiments are not limited to any particular industry.

Disclosed embodiments include a system for verifying a user identity.The system comprises one or more memory devices storing instructions andone or more processors configured to execute the instructions. Theprocessors are configured to receive information associated with anaccount of a user. The processors are further configured to generate afirst profile, where the first profile being related to the user. Theprocessors also receive an indication that the account is accessed by anaccessor through an accessor device; and receive, from the accessordevice, identity data comprising a plurality of data subsets associatedwith the accessor. The processors are configured to store the datasubsets in respective clusters. The processors are further configuredgenerate cluster analyses by analyzing the data subsets in respectiveclusters; and output the cluster analyses to node instances that weighsthe cluster analyses outputs. The processors also generate a secondprofile, the second profile related to the accessor and being based onthe received identity data and weighted cluster analysis. And theprocessors are configured to determine a likelihood factor that theaccessor is the user based on a comparison of the first profile and thesecond profile.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory only,and are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate several embodiments and, togetherwith the description, serve to explain the disclosed principles. In thedrawings:

FIG. 1 is a block diagram of an exemplary remote access system,consistent with disclosed embodiments.

FIG. 2 is a diagram of an exemplary user device, consistent withdisclosed embodiments.

FIG. 3 is a diagram of an exemplary account provider system, consistentwith disclosed embodiments.

FIG. 4 is a diagram of an exemplary verification system, consistent withdisclosed embodiments

FIG. 5 is a diagram of an exemplary process for modeling and analyzingan accessor profile and a user profile.

FIG. 6 is a diagram of an exemplarily process for modeling, consistentwith disclosed embodiments.

FIG. 7 is a flowchart of an exemplary process for utilizing enhanceddata to verify the identity of an accessor.

FIG. 8 is a flowchart of another exemplary process for utilizingenhanced data to verify the identity of an accessor.

DETAILED DESCRIPTION

An initial overview of machine learning is first provided immediatelybelow and then specific exemplary embodiments of systems and methods forverifying a user identity are described in further detail. The initialoverview is intended to aid in understanding some of the technologyrelevant to the systems and methods disclosed herein, but it is notintended to limit the scope of the claimed subject matter.

In the world of machine prediction, there are twosubfields—knowledge-based systems and machine learning systems.Knowledge-based approaches rely on the creation of a heuristic orrule-base which is then systematically applied to a particular problemor dataset. Knowledge-based systems make inferences or decisions basedon an explicit “if-then” rule system. Such systems rely on extracting ahigh degree of knowledge about a limited category in order to virtuallyrender all possible solutions to a given problem. These solutions arethen written as a series of instructions to be sequentially followed bya machine.

Machine learning, unlike the knowledge-based programming, providesmachines with the ability to learn through data input without beingexplicitly programmed with rules. For example, as just discussed,conventional knowledge-based programming relies on manually writingalgorithms (i.e., rules) and programming instructions to sequentiallyexecute the algorithms. Machine learning systems, on the other hand,avoid following strict sequential programming instructions by makingdata-driven decisions to construct their own rules. The nature ofmachine learning is the iterative process of using rules, and creatingnew ones, to identify unknown relationships to better generalize andhandle non-linear problems with incomplete input data sets.

Examples of machine learning techniques include, but are not limited todecision tree learning, association rule learning, inductive logicprogramming, support vector machines, clustering, Bayesian networking,reinforcement learning, representation learning, similarity and metriclearning, spare dictionary learning, rule-based machine learning, andartificial neural networks.

One such machine learning technique involves the use of “artificialneural networks.” Artificial neural networks are computational systemsthat enable computers to essentially function in a manner analogous tothat of the human brain. Generally, a neural network is aninformation-processing network and an artificial neural network is aninformation-processing network inspired by biological neural systems.Artificial neural networks create non-linear connections betweencomputation elements (i.e., “nodes” and “clusters”) operating inparallel and arranged in patterns. The nodes are connected via variableweights, typically adopted during use, to improve performance. Thus, insolving a problem or making a prediction, an artificial neural networkmodel can explore many hypotheses and permutations by simultaneouslyusing massively parallel networks composed of many computationalelements connected by links with variable weights.

The function of the artificial neural network is determined by thenetwork structure, connection strengths, and the processing performed atthe computation elements. The nodes can be “neuron-like” computationalelements that output a signal based on the sum of their inputs, theoutput being the result of an activation function. Much like abiological neural network, an artificial neural network thus has aplurality of computation elements interconnected with weightedcommunication bridges. By adjusting the weight values of the connectionsbetween computation elements in a network, one can match certain inputswith desired outputs. The respective weights assigned to particularcomputation elements are dynamic and can be modified in response totraining. Through this weighted connection structure, the network“communicates,” identifies unknown relationships, and “learns” thecharacteristics of general input categories. Thus, artificial neuralnetworks do not require pre-programming that anticipates all possiblevariants of the input data they receive.

As already discussed, a neural network is not programmed; instead, it is“taught.” Of course, there are many variations for teaching anartificial neural network. Some networks are taught through examples,whereas others extract information directly from the input data. The twovariations are called “supervised” and “unsupervised” learning. Insupervised systems, a learning algorithm is incorporated to adjust theweights of the connections in the network for optimal performance, basedon the presentation of a predetermined set of correct stimulus-responsepairs. Rather than attempting to anticipate every possible exhibition ofdata, artificial neural networks attempt to recognize patterns of dataand make decisions based on the conformity with historical patternshaving known attributes. The training of neural networks involves aniterative process where individual weights between nodes are repeatedlyadjusted until the system converges to produce a derived output. Whiletraining a neural network may be time consuming, it is not laborintensive and avoids the necessity to develop an explicit algorithm. Inessence, after training, the architecture of the neural network embodiesthe algorithm. The techniques and algorithms for training neuralnetworks are numerous and diverse, each having certain advantages anddisadvantages.

In contrast to supervised systems, unsupervised systems require nohistorical training data to train the system. The artificial neuralnetwork is autonomous and as such it can automatically determineproperties about data and reflect these properties in an output.Unsupervised neural networks take into consideration not only theproperties of individual events producing data, but the event'srelationship with other events and the event's relationship topredetermined concepts which characterize the event collection. Oneunsupervised learning technique, conjunctive conceptual clustering, wasfirst developed in the early eighties by Stepp and Michalski. A detailedexplanation of the technique is disclosed in their article; Michalski,R. S., Stepp, R. E. “Learning from Observation: Conceptual Clustering”,Chapter 11 of Machine Learning: an Artificial Intelligence Approach,eds. R. S. Michalski, J. G. Carbonell and T. M. Mitchell, San Mateo:Morgan Kaufmann, 1983.

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings and disclosedherein. Wherever convenient, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

The disclosed embodiments relate to systems and methods for utilizingenhanced data to verify a user account accessor's identity. While somesolutions currently exist for verifying identities, such solutionstypically only implement user-defined security. This is inefficient andrelies on a user to actively provide secure access to the account.Furthermore, current solutions do not effectively utilize machinelearning to properly analyze user accessing behavior, or model accessingbehavior.

There exist substantial untapped user data sources that can be utilizedto provide improved seamless user verification. One such area is to tapinto every instance of the user accessing his account and to farm theactive and passive data sets generated by such user accesses. Every useraccess attempt generates multiple active and passive data points, bothfrom himself and from the accessing device; however, the majority ofthese data points go unused. For example, in order to counter the user'slack of diligence in setting up known devices, a system could analyzepassive information from those devices used by the user to successfullyaccess the user account. Such passive device information may include thedevice operating system, model number, process chipsets, browser type,etc. Likewise, the system could utilize user information such as theuser's active error rate, mouse speed, scroll speed, typing speed, eyemovement rate, etc. Additionally, users also have static, or passivedata, such as fingerprints, iris scans, palm vein pulse, heartbeat, andother biometric data. This static data could be implemented in a systemto provide a seamless verification system that is constantly collectingdata and constantly analyzing the user's access to their account.

The following description provides examples of systems and methods forverifying the identity of a user. The arrangement of components shown inthe figures is not intended to limit the disclosed embodiments, as thecomponents used in the disclosed systems may vary.

FIG. 1 depicts an illustrative remote access system 100 utilizinginformation collected for enhanced user identity verification inaccordance with aspects of an embodiment of the present disclosure.System 100 includes an accessing device 110, which can be any userdevice discussed above, in communication with a network 120 which is infurther communication with an account provider system 130. The means ofcommunication between device 110, network 120, system 130 can vary andthe particular combination can also vary, such that device 110 maycommunicate directly with system 130 and vice versa. It will also beunderstood that device 110, and devices associated with system 130, mayalso communicate directly with network 120 or through network 120.Account provider system 130 also contains devices that store, or areassociated with, identity verification application 140 and user account150.

Through these illustrative components, system 100 collects and utilizesdata for enhanced user identity verification. For instance, bycollecting passive and active data from a user, or account accessor, anddata from accessing device 110, account provider system 130 can furtherverify that an access attempt to user account 150 is authorized, byverifying the accessor identity with identity verification application140. Account provider system 130 may further use collected and analyzeddata to continuously update historical records of the user and verifythe user identity based on the passive, active, and behavioral data. Inturn, the account provider system 130 may provide a more seamless andsecure account access experience.

FIG. 2 illustrates an exemplary configuration of accessing device 110,consistent with disclosed embodiments. Various types of accessing device110, such as a cell phone, a tablet, a mobile computer, a desktop, etc.may be implemented in system 100. As shown, accessing device 110includes a display 111, an input/output (“I/O”) device 112, one or moreprocessors 113, and a memory 114 having stored therein one or moreprogram applications 115, such as an account app 116, and data 117.Accessing device 110 also includes an antenna 118 and one or moresensors 119. Display 111, I/O devices 112, processor(s) 113, memory 114,antenna 118, or sensor(s) 119 may be connected to one or more of theother devices depicted in FIG. 1B. Such connections may be accomplishedusing a bus or other interconnecting device(s).

Processor 113 may be one or more known processing devices, such as amicroprocessor from the Pentium™ or Atom™ families manufactured byIntel™, the Turion™ family manufactured by AMD™ the Exynos™ familymanufactured by Samsung™, or the Snapdragon™ family manufactured byQualcomm™. Processor 113 may constitute a single core or multiple coreprocessors that executes parallel processes simultaneously. For example,processor 113 may be a single core processor configured with virtualprocessing technologies. In certain embodiments, processor 113 may uselogical processors to simultaneously execute and control multipleprocesses. Processor 113 may implement virtual machine technologies, orother known technologies to provide the ability to execute, control,run, manipulate, store, etc., multiple software processes, applications,programs, etc. In another embodiment, processor 113 may include amultiple-core processor arrangement (e.g., dual, quad core, etc.)configured to provide parallel processing functionalities to allow smartdevice 110 to execute multiple processes simultaneously. One of ordinaryskill in the art would understand that other types of processorarrangements could be implemented that provide for the capabilitiesdisclosed herein.

I/O devices 112 may include one or more devices that allow accessingdevice 110 to receive input from a user and provide feedback to theuser. I/O devices 112 may include, for example, one or more buttons,switches, speakers, microphones, stylus, or touchscreen panels. In someembodiments, I/O devices 112 may be manipulated by the user to inputinformation into accessing device 110.

Memory 114 may be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, non-removable, or other type of storage deviceor tangible (i.e., non-transitory) computer-readable medium that storesone or more program applications 115 such as account app 116, and data117. Data 117 may include, for example, customer personal information,account information, and display settings and preferences. In someembodiments, account information may include items such as, for example,an alphanumeric account number, account label, account balance, accountissuance date, account expiration date, account issuer identification, agovernment ID number, a room number, a room passcode, and any othernecessary information associated with a user and/or an accountassociated with a user, depending on the needs of the user, entitiesassociated with network 120, and/or entities associated with system 100.

Program applications 115 may include operating systems (not shown) thatperform known operating system functions when executed by one or moreprocessors. By way of example, the operating systems may includeMicrosoft Windows™, Unix™, Linux™, Apple™, or Android™ operatingsystems, Personal Digital Assistant (PDA) type operating systems, suchas Microsoft CE™, or other types of operating systems. Accordingly,disclosed embodiments may operate and function with computer systemsrunning any type of operating system. Accessing device 110 may alsoinclude communication software that, when executed by processor 113,provides communications with network 120, such as Web browser software,tablet, or smart hand held device networking software, etc. Accessingdevice 110 may be a device that executes applications for performingoperations consistent with disclosed embodiments, such as a tablet,mobile device, laptop, desktop, or smart wearable device.

Program applications 115 may include account app 116, such as an accountapp for activating, setting up, and configuring user access to network120 and account provider system 130. In some embodiments, account app116 may include instructions that cause processor 111 to connect withnetwork 120 and account provider system 130.

Accessing device 110 may also store in memory 114 data 117 relevant tothe examples described herein for system 100. One such example is thestorage of user identifying information like a username/password, userbiometric data, etc. or passive device 110 information like browsertype, software version, connection type, etc., obtained from sensors119. Data 117 may contain any data discussed above relating to thehistorical data used to teach the artificial neural network model oranalyzed input. The data 117 may be further associated with informationfor a particular user or particular user device.

Sensors 119 may include one or more devices capable of sensing theenvironment around accessing device 110, and/or movement of accessingdevice 110, and even sensing data gathered by input/output 112. In someembodiments, sensors 119 may include, for example, an accelerometer, ashock sensor, a gyroscope, a position sensor, a microphone, a camera, anambient light sensor, a temperature sensor, and/or a conductivitysensor. In addition, sensors 119 may include devices for detectinglocation via systems such as a Global Positioning System (GPS) sensor, aradio frequency triangulation system based on cellular or other suchwireless communication, and/or other systems for determining thelocation of accessing device 110.

Antenna 118 may include one or more devices capable of communicatingwith network 120. One such example of wireless communication is anantenna wirelessly communicating with network 120 via cellular data orWi-Fi. Although communication between accessing device 110 and network120 is shown as wireless communication, communication could also occurusing wired communication via, for example, an Ethernet terminal (notshown).

In certain embodiments, device 110 may include a power supply, such as abattery (not shown), configured to provide electrical power to accessingdevice 110.

Returning to FIG. 1 , network 120 may comprise any type of computernetworking arrangement used to exchange data. For example, network 120may be the Internet, a private data network, virtual private networkusing a public network, and/or other suitable connection(s) that enablessystem 100 to send and receive information between the components ofsystem 100. Network 120 may also include a public switched telephonenetwork (“PSTN”) and/or a wireless network such as a cellular network,WiFi network, or other known wireless network capable of bidirectionaldata transmission. Network 120 may also comprise any local computernetworking used to exchange data in a localized area, such as WiFi,Bluetooth™, Ethernet, Radio Frequency, and other suitable networkconnections that enable components of system 100 to interact with oneanother.

FIG. 3 shows an exemplary configuration of account provider system 130consistent with disclosed embodiments. It will be further apparent thataccount provider system 130 can be further associated with a financialservice provider, a merchant, a vendor, or any other entity thatmaintains a user account 150 (FIG. 1 ). In one embodiment, accountprovider system 130 may optionally include one or more processors 131,one or more input/output (I/O) devices 132, and one or more memories133. In some embodiments, system 130 may take the form of a server,general purpose computer, mainframe computer, or the like. In someembodiments, system 130 may take the form of a mobile computing devicesuch as a smartphone, tablet, laptop computer, or the like.Alternatively, system 130 may be configured as a particular apparatus,device, dedicated circuit, or the like, based on the storage, execution,and/or implementation of the software instructions that perform one ormore operations consistent with the disclosed embodiments.

Processor(s) 131 may include one or more known processing devices, suchas mobile device microprocessors, desktop microprocessors, servermicroprocessors, or the like. The disclosed embodiments are not limitedto a particular type of processor.

I/O devices 132 may be one or more devices configured to allow data tobe received and/or transmitted by system 130. I/O devices 132 mayinclude one or more digital and/or analog devices that allow system 130to communicate with other machines, devices, and systems, such as othercomponents and devices of system 100. For example, I/O devices 132 mayinclude a screen for displaying messages to a user (such as a customer,a retail venue manager, or a financial service provider employee). I/Odevices 132 may also include one or more digital and/or analog devicesthat allow a user to interact with system 100, such as a touch-sensitivearea, keyboard, buttons, or microphones. I/O devices 132 may alsoinclude other components known in the art for interacting with a user.I/O devices 132 may also include one or more hardware/softwarecomponents for communicating with other components of system 100. Forexample, I/O devices 132 may include a wired network adapter, a wirelessnetwork adapter, a cellular network adapter, or the like. Such networkcomponents enable device 130 to communicate with other devices of system100 to send and receive data.

Memory 133 may include one or more storage devices configured to storeinstructions usable by processor 131 to perform functions related to thedisclosed embodiments. For example, memory 133 may be configured withone or more software instructions, such as one or more programapplications 134 that perform one or more operations when executed byprocessor 131. The disclosed embodiments are not limited to separateprograms or computers configured to perform dedicated tasks. Forexample, memory 133 may include a single program or multiple programsthat perform the functions of accessing device 110 or network 120.Memory 133 may also store data 135 that is used by the one or moreapplications 134.

In certain embodiments, memory 133 may store software executable byprocessor 131 to perform methods, such as the methods represented by theflowcharts depicted in FIGS. 5-7 and/or the methods associated with userinterface (e.g., display 111) discussed above with reference to FIG. 2 .In one example, memory 133 may store program applications 134.Applications 134 stored in memory 133, and executed by processor 131,may include a financial service app that causes processor 131 to executeprocesses related to financial services provided to users including, butnot limited to, processing credit and debit card transactions, checkingtransactions, processing payments for goods, price checking goods,analyzing customer purchasing behavior and adjusting good pricing basedon the analysis, adjusting good pricing, authorizing identity of user,verifying transactions, and/or updating the user account. Memory 133 maystore identity verification application 140. In some examples, programapplications 134 may be stored in an external storage device, such as acloud server located outside of network 120, and processor 131 mayretrieve and execute the externally stored programs 134.

Account provider system 130 may be used to store data 135 relevant toexamples described herein for system 100. One such example is thestorage of input data received by accessing device 110 from input/outputdevices 112 (microphones, keyboards, touch screens, cameras, webcameras, mouse, biometric sensors, location based sensors,accelerometers, etc. discussed throughout here). Data 135 may containany data discussed above relating to the communication of user-basedinputs. In addition, data 135 may contain user account 150 data such asprevious accessor behavioral patterns, biometric data, account data,typing pattern, mouse movements, finger pressure rates, reading rates,typing error rates, portions of account previously accessed, type ofdevice accessing user account, and/or the accessing device staticinformation. The data 135 associated with particular user may alsocontain associated information for accessing device 110. Data 135 mayalso include a modeled determination and analysis.

Account provider system 130 may include at least one database 136.Database 136 may be a volatile or non-volatile, magnetic, semiconductor,tape, optical, removable, nonremovable, or other type of storage deviceor tangible (i.e., non-transitory) computer readable medium. Forexample, database 136 may include at least one of a hard drive, a flashdrive, a memory, a Compact Disc (CD), a Digital Video Disc (DVD), or aBlu-ray™ disc.

Database 136 may store data, such as data 135 that may be used byprocessor 131 for performing methods and processes associated withdisclosed examples. Data stored in database 136 may include any suitabledata, such as information relating to a user, a mobile accessing device110, information relating to accessor behavior, and information modeledin the identity verification application 140, and/or user account 150.Although shown as a separate unit in FIG. 3 , it is understood thatdatabase 136 may be part of memory 133, or an external storage devicelocated outside of system 100. At least one of memory 133, and/ordatabase 136 may store data and instructions used to perform one or morefeatures of the disclosed examples. At least one of memory 133, and/ordatabase 136 may also include any combination of one or more databasescontrolled by memory controller devices (e.g., server(s), etc.) orsoftware, such as document management systems, Microsoft SQL databases,Share Point databases, Oracle™ databases, Sybase™ databases, or otherrelational databases. Account provider system 130 may also becommunicatively connected to one or more remote memory devices (e.g.,databases (not shown)) through network 120, or a different network. Theremote memory devices may be configured to store information and may beaccessed and/or managed by system 100. Systems and methods consistentwith disclosed examples, however, are not limited to separate databasesor even to the use of a database.

The components of account provider system 130 may be implemented as adevice in hardware, software, or a combination of both hardware andsoftware, as will be apparent to those skilled in the art. For example,although one or more components of system 130 may be implemented ascomputer processing instructions, all or a portion of the functionalityof system 130 may be implemented instead in dedicated electronicshardware.

Account provider system 130 also store identity verification application140 and user account 150. Through processor(s) 131, account providersystem 130 runs identity verification application 140 by performingmethods and processes associated with disclosed examples described morefully below. Identity verification application 140 may analyze receiveddata 135 for users and accessing devices including, but not limited to,username/password and previous accessor behavioral patterns associatedwith the user account 150; biometric, account, typing pattern, mousemovement, finger pressure rate, or typing error rate data associatedwith the user; portions of user account 150 previously accessed; theaccessing device 110 static information like type of device, deviceoperating system, device serial number, type of connection; dataobtained from sensors 119 or location based data; historical data storedin memory 114 (and/or memory 133); as well as any analysis conducted ormodeled by identity verification application 140. In some examples,identity verification application 140 may be stored in an externalstorage device, such as a cloud server located outside of network 120and account provider system 130, and processor 131 may execute theidentity verification application 140 remotely.

User account 150 is a subset of account provider system 130 and isanalyzed by identity verification application 140. User account 150 maybe stored locally in memory 133, or it may be stored in an externalstorage device, such as a cloud server located outside of network 120,and processor 131 may configure the user account 150 remotely. Data 135is further associated with multiple users and each respective user has auser account 150 that contains their associated financial serviceaccount, their data, and the analysis of identity verificationapplication 140.

FIG. 4 illustrates an exemplary verification 400 system. An accessorattempting to access user account 150 may attempt to access the accountthrough any accessing device 110. Verification system 400 demonstratesseveral examples of accessing devices such as a cell phone 110 a, atablet 110 b, and a desktop computer 110 c. Each device 110 a-110 c mayaccess user account 150 through means discussed throughout here, andeven through network 120. Additionally, each device 110 a-110 c receivesinput from input/output devices 112 a-112 l and sensors 119 a-119 f.Communication between devices 110 a-110 c and account provider system130 may occur through various means through network 120. Appropriateforms of communication include near-field communication (NFC), Wi-Fi,Bluetooth, cellular, and/or other such forms of wireless communication,as well as wired communication, discussed herein. In certainembodiments, devices 110 a-110 c may include a power supply, such as abattery, configured to provide electrical power to one or morecomponents of devices 110 a-110 c, such as processer 113, a memory 114,and a communication device 118.

Once received, account provider system 130 may further analyze data from112 a-112 l and 119 a-119 f using identity verification application 140.Identity verification application 140 may verify and grant accessors'access to user account 150 from devices 110 a-110 c. Additionally,devices 110 a-c may continuously transmit data from 112 a-112 l and 119a-119 f to account provider system 130, and identity verificationapplication 140 may continuously update its analysis based on this data.

A person of ordinary skill will now understand that the data received bydevices 110 a-110 c from 112 a-112 l and 119 a-119 f can be altered,using various combinations, and that the system is not limited to thedevices illustrated in FIG. 4 . For instance, desktop 110 c may befurther equipped with a touch screen 112 input/output device as well.Alternatively, cell phone 110 a may be further equipped with anaccelerometer 119 sensor to detect accessor's acceleration in any giventhree-axis.

FIG. 5 is a flow chart of an exemplary process verifying accessor/useridentity by modeling and analyzing an accessor profile and a userprofile. This exemplary process may be utilized by identity verificationapplication 140, by collecting data from users, accessors, and accessingdevices 110. The process begins by collecting input at steps 501-505from a user, from accessors, and from accessing devices 110 a-110 n (notshown). The input includes, but not limited to, username/password andprevious accessor behavioral patterns associated with the user account150; biometric, account, typing pattern, mouse movement, finger pressurerate, or typing error rate data associated with the user; portions ofuser account 150 previously accessed; the accessing device 110 staticinformation such as type of device, device operating system, deviceserial number, type of connection; and data obtained from sensors 119 orlocation based data; historical data stored in memory 114 (and/or memory133). Additionally, the input may further include any analysispreviously conducted or modeled by identity verification application140. As discussed above, accessing devices 110 a-110 n and accountprovider system 130 store active and passive data associated withaccessors and the user collected by multiple devices 110 a-110 n fromnumerous user account 150 access attempts. Each step, 501-305, is inreal time and continuous, and may be associated with a particularaccessor or accessing device 110.

Next, at step 510, identity verification 500 process creates an accessorprofile based on accessor behavior as defined by received input datasets 501-505. Step 510 is performed in real time and continuouslyreceives data from devices 110 a-110 n. Then at step 520, identityverification 500 process compares the accessor profile from step 510with a user profile reflecting historical behavior typical of a specificuser. The comparison at step 520, between the accessor profile and userprofile, is also performed in real time and continuously updating.

At step 530, identity verification process 500 analyzes the receiveddata from steps 501-505 to verify that the accessors' identity matchesthe user associated with user account 150 and the user profile from step520. The result of this analysis may be in the form of a likelihoodfactor based on the comparison at step 520 between the accessor profileand the user profile. Further, identity verification process 500 mayemploy various machine learning techniques to analyze the collected datafrom steps 501-505. Examples of machine learning techniques includedecision tree learning, association rule learning, artificial neuralnetworks, inductive logic programming, support vector machines,clustering, Bayesian networking, reinforcement learning, representationlearning, similarity and metric learning, spare dictionary learning,rule-based machine learning, etc.

A person of ordinary skill will now understand that through theseverification steps, process 500 further facilitates the goal providing amore seamless and secure account access experience. By utilizingaccessor passive and active data sets, and machine learning, identifyverification process 500 may further assist the service provider anduser by eliminating the need for time consuming and burdensomemulti-stage verification procedures. The analytics can determineaccurate identities without impeding user's access to the content oftheir user account 150.

FIG. 6 illustrates an exemplary accessor profile analysis modelingprocess. Like FIG. 5 , this exemplary accessor profile analysis modelingprocess may be utilized by identity verification application 140, bycollecting data from users, accessors, and accessing devices 110. WhereFIG. 5 depicts a generic modeling process, FIG. 6 illustrates the morespecific computation element based, i.e., node/cluster, artificialneural network as discussed above. Upon determination that an accessoris attempting to access user account 150, system 100 will beginanalyzing the received input no matter whether it is passive or activedata. For instance, system 100 may receive input data from accessingdevice 110 a (cell phone), device 110 b (tablet), or device 110 c(desktop computer). Each accessing device 110 a-110 c may have its ownset of input data 501, i.e., 501 a-501 n, 502 a-502 n, and 503 a-503 n.Each device 110 a-110 c may access user account 150 through meansdiscussed throughout here, and even through network 120. For example,referencing FIG. 4 , cell phone accessing device 110 a may output dataassociated with its touch screen 112 c or microphone 112 d; while tabletaccessing device 110 b may output data associated with its applicationkeyboard 112 f or camera 112 h; and desktop computer device 110 c mayoutput data associated with its web camera 112 i or keyboard 112 j. Oneskilled in the art will understand that the various output combinationsfor devices 110 a-110 c and their respective input/output devices 112a-112 n or sensors 119 a-119 n can vary.

Returning now to FIG. 6 , received input data sets 501, 502, and 503 maybe further sub-categorized by data type character traits and analyzed byclusters 600 a-600 i. For instance, passive device data for all devices110 a-110 c, such as the browser type, operating software version,connection type, model number, processor chip types, memory types, etc.,may be collected by cluster 600 a. Cluster 600 b may collect all datafrom devices 110 a-110 c pertaining to entered usernames and/orpasswords. Cluster 600 c may gather and analyze typing patterns fromdevices 110 a-110 c. Cluster 600 d may receive and analyze mousemovement data; while cluster 600 e may receive and analyze fingerpressure data. Cluster 600 f may receive and analyze accessor overallerror rate data. Additionally, cluster 600 g may receive data indicatingthe portions of user account 150 with which the accessor interacts.Cluster 600 h may receive and analyze biometric data from devices 110a-110 c; and cluster 600 i may receive data indicative of the accessor'stext reading rate.

Once again, it should be understood that while FIG. 6 depicts certaincombinations of datasets 501 a-501 n, 502 a-502 n, and 503 a-503 n, theinvention is not limited to this depiction. For instance, althoughdesktop computer 110 c may be the only device with a video camera ableto analyze eye movement, and hence the only dataset analyzed by cluster600 i for determining accessor reading rate, it should be furtherunderstood that cell phone device 110 a may also be equipped with acamera capable of outputting a data subset to be analyzed by cluster 600i. Similarly, desktop computer device 110 c may not be the only deviceoutputting mouse movement data to be analyzed by cluster 600 f (i.e., atablet may have a mouse attached to it).

Additionally, datasets from devices 110 a-110 c may be further weightedbased upon iterative analysis, similar to steps 510 and 520 from FIG. 5. For instance, it may be determined that user account 150 is typicallyaccessed by a particular cell phone device 110 a, thus the received datasubsets from 110 a (i.e., 501 a-501 n) will be weighted heavily comparedto other devices. And the clusters themselves could be further groupedinto tiers based on the importance and weight of their respectiveanalysis. For instance, successful access to user account 150 fromcertain devices with passive info, i.e., cluster 600 a, may be groupedtogether with devices commonly entering the most accurateusername/password combinations from cluster 600 b. In one embodiment,clusters 600 a-600 b containing passive device data may be groupedtogether to form a first tier 640. While clusters 600 c-600 f, generallyanalyzing the accessor active data may be grouped in a second tier 641.And accessor passive data, generally clusters 600 g-600 i may also begrouped into a third tier 642.

The collection, and subsequent analysis, occurs in real time,contemporaneously, and continuously while the accessor accesses (andattempts to access) user account 150.

Once separated and categorized, the data subsets may be analyzedtogether in their respective clusters 600 a-600 i. As already addressed,depending on the received and categorized data subsets, the clusters mayanalyze information from multiple accessing devices 110, from multipleinput/output 112 devices, and/or by multiple sensors 119; and thecluster analysis itself may be further separated into tiers. Clusters,with techniques generally discussed above described in the artificialmachine learning section, will form connections between data subsets andoutput an analysis 610 a-610 i to a final node 620. Further, theclusters 600 a-600 i and node 620 may be program constructs designed tointeract with, or be a part of, identity verification application 140.

Node 620, much like the clusters, will collect cluster analysis 610a-610 i from each cluster with pre-determined weights and tier scales.Each individual cluster and/or tier of cluster output analysis 610 a-610i may carry a unique weighted multiply factor. For instance, as depictedin FIG. 6 , node 620 may weight outputs from clusters 600 a, 600 b, and600 h (i.e., 610 a—device passive information, 610 b—username/passworddata, and 610 h—biometric data) more heavily in its analysis than theother cluster analyses. Like the cluster weighting, it will beunderstood by a person skilled in this art that this depiction ofassigned weights may be adjusted. In general, the analyzed data subsetsare outputted by the clusters to the node 620 for final weighting,compilation, and analysis. The node 620 analysis will continuouslyreceive cluster outputs 610 a-610 i and analyze the outputs based onpre-established weighted factors and interconnectivity associationsbetween the clusters and/or tiers of clusters.

Finally, the node 620 will output its analysis 630. Node 620 willdetermine the similarity of the current accessor behavior, derived fromthe passive and/or active data from the accessing device 110 and fromthe accessor itself (i.e., 501 a-503 n), and compare the currentaccessor behavior against past stored historical data. The storedhistorical data may consist of previous accessor behavior and userbehavior. The accumulation of this similarity comparison analysis 630will be the quantified identity verification likelihood factor 530.

It will be further understood that the pre-determined factors, such asthe likelihood factor 530 threshold, the cluster and node weights, thedevice weights, the tier weights, and tier combinations may be adjustedbefore analysis or continuously and contemporaneously during theanalysis.

FIG. 7 is a flowchart of an exemplary process embodiment for utilizing,analyzing, and modeling information collected for enhanced verificationof accessors' data and attempted access of user account 150. The systembegins at step 710, where account provider system 130 receives initialinformation from a user to be associated with user account 150. Aspreviously discussed, user account 150 may be set up and maintained by afinancial service provider or any entity that provides user accounts fortheir customers. As such, account provider system 130 receives initialaccount information required, such as financial records, userpreferences, contact information, etc., as well as, information toverify user identity, such as for example username/password, known userdevices, biometric data, security questions, etc.

The information received by account provider system 130 in step 710 maybe received via network 120 from device 110. Alternatively, in step 710,the account provider system 130 may receive the initial informationassociated with an account of the user through other means, such as,from a database of another entity (e.g., a credit bureau), or fromanother storage device connected to network 120. In addition, at step710, the account provider system 130 may receive active and passivedata, much like the acquired data sets from steps 501-505 from FIG. 5 ,from the user and user accessing device 110.

At step 720, account provider system 130 generates an initial, that is,a user profile for the user. This initial profile will be based onaccessing behavior of the user. Much like steps 501-505 from FIG. 5 ,step 720 will collect and utilize active and passive data from theinitial user interaction from step 710. This collection of active andpassive data may even occur while the user creates user account 150. Instep 710, the active and passive data will include data collected fromthe user's accessing device, such as, username/password; biometric,account, typing pattern, mouse movement, finger pressure rate, or typingerror rate data associated with the user; portions of user account 150previously accessed; the accessing device 110 static information liketype of device, device operating system, device serial number, type ofconnection; data obtained from sensors 119 or location based data; aswell as, historical data stored in memory 114.

After receiving information associated with an account of the user atstep 710, and after generating the user profile at step 720, the system100 further receives, at step 730, an indication that the user account150 is accessed by an accessor. In one embodiment, the account providersystem 130 may receive an indication of a user account 150 accessattempt by accessing device 110. Alternatively, account provider system130 may receive an indication, from a remote source, that an accessor isattempting to log into user account 150. At step 730, it is unverifiedwhether accessor is indeed the user.

At step 740, the system 100 further processes received identity datasets from the source associated with the attempted access indicated atstep 730. Step 740 may be conducted by account service provider 130which receives the identity data sets through network 120 from aplurality of accessing devices 110 a-110 n. In addition, step 740 occursin real time and continuously while the accessor accesses (and attemptsto access) user account 150. Alternatively, step 740 may occurcontemporaneously for several devices 110 a-n, several input/outputdevices 112, and/or several sensors 119. Step 740 receives identity datasets from multiple sources and accessing devices 110 a-110 n and, likestep 710, the data sets may be active or passive information from theaccessor and accessor device.

At step 750, the received identity data sets are separated into subsetsand the subsets are analyzed by clusters. Much like the steps describedin FIG. 6 , the received identity data may first be categorized. Thedata may be separated by type of data from a particular accessing device110, by a type of input/output 112 device, and/or by a sensor 119. Thedata may be further separated by active and/or passive acquisitiontechniques. Once separated and categorized, the data subsets may beanalyzed together in clusters. Depending on the received and categorizeddata subsets, the clusters may analyze information from multipleaccessing device 110 sources, from multiple input/output 112 devices,and/or by multiple sensors 119. The cluster analysis itself may befurther separated into tiers. Each individual cluster and/or tier ofclusters may carry a unique weighted multiply factor. Additionally, thereceived data subsets may also carry unique weighted multiply factors aswell. The step 750 analysis will continuously receive and analyze thedata subsets based on pre-established weighted factors andinterconnectivity associations between clusters. Further, the clustersmay be program constructs designed to interact with, or be a part of,identity verification application 140.

At step 760, the analyzed data subsets are outputted by the clusters toa node for final weighting, compilation, and analysis. Much like step750, the node analysis itself may be further separated into tiers. Eachindividual node and/or tier of nodes may analyze output clusters andassign unique weighted multiply factors. The step 760 analysis willcontinuously receive cluster outputs, and analyze the outputs based onpre-established weighted factors and interconnectivity associationsbetween the clusters and/or tiers of nodes. Further, the node and/ortiers of nodes may be program constructs designed to interact with, orbe a part of, identity verification application 140.

At step 770, a second, or accessor, profile reflecting accessor behavioris generated based on the received data from step 740, cluster analysisfrom step 750, and node weighting from step 760. This accessor profilewill be based on the accessor's accessing behavior. Much like steps501-505 from FIG. 5 , and step 720, step 770 generates the accessorprofile based on the collected active and passive data continuously andcontemporaneously from step 740. The active and passive data willinclude data collected from the accessors' accessing device from step740, such as, username/password; biometric, account, typing pattern,mouse movement, finger pressure rate, or typing error rate dataassociated with the accessor; portions of user account 150 previouslyaccessed; the accessing device 110 static information like type ofdevice, device operating system, device serial number, type ofconnection; data obtained from sensors 119 or location based data; aswell as, historical data stored in memory 114.

The system 100 then compares the user profile from step 720 and theaccessor profile from step 770 and determines a likelihood factor thatthe two profiles match at step 780. Based on this determined likelihoodfactor, the account provider system 130 and/or identity verificationapplication 140 may determine that the accessor is indeed the user andauthorize the access of user account 150.

In one embodiment, system 100, at step 780, may determine that the userprofile and the accessor profile are not identical, but are similar.Further, at step 780, the system 100 will determine the likelihood thatthe two profiles are similar within a confidence interval. If system 100determines the two profile similarities are above a pre-determinedthreshold factor, then account provider system 130 and/or identityverification application 140 may grant access to user account 150.Alternatively, if accessor activity drops the two profile similaritiesbelow the pre-determined threshold factor during access of user account150, permitted access to user account 150 may be terminated.

FIG. 8 is a flowchart of another exemplary process embodiment forutilizing, analyzing, and modeling information collected for enhancedverification of accessors' data and attempted access of user account150. The system begins at step 810, where user provides initialinformation associated with their user account 150. As previouslydiscussed, the user account 150 may be associated with a financialservice provider or any entity that associates user accounts with theircustomers. As such, user may provide initial information required by theaccount provider, such as, financial records, user preferences, contactinformation, etc., as well as information to verify user identity. Instep 810, user may provide the account provider system 130 with theinitial information through network 120 while using device 110.Alternatively, in step 810, the account provider system 130 may receivethe initial information associated with an account of the user throughother means, such as, from another entity's database, or from anotherstorage device connected to network 120. In addition, at step 810, theaccount provider system 130 may receive active and passive data, muchlike the acquired data sets from steps 501-505 from FIG. 5 , from theuser and user accessing device 110.

At step 820, the account provider system 130 generates an initial, oruser, profile for the user. This initial profile will be based on theuser accessing behavior. Much like steps 501-505 from FIG. 5 , step 820will collect and utilize active and passive data from the initial userinteraction from step 810. The active and passive data will include datacollected from the user's accessing device from step 810, such as,username/password; biometric, account, typing pattern, mouse movement,finger pressure rate, or typing error rate data associated with theuser; portions of user account 150 previously accessed; the accessingdevice 110 static information like type of device, device operatingsystem, device serial number, type of connection; data obtained fromsensors 119 or location based data; as well as, historical data storedin memory 114.

After receiving information associated with an account of the user atstep 810, and after generating an initial profile for the user at step820, the system 100 further receives, at step 830, an indication thatthe user account 150 is accessed by an accessor. In one embodiment, theaccount provider system 130 may receive an indication of an attemptedaccess to user account 150 by accessing device 110. Alternatively,account provider system 130 may receive an indication, from a remotesource, that an accessor is attempting to log into user account 150. Atstep 830, it is unverified whether accessor is indeed the user.

At step 840, the system 100 further processes received identity datasets from the source associated with the attempted access indicated atstep 830. Step 840 may be conducted by account service provider 130which receives the identity data sets through network 120 from aplurality of accessing devices 110 a-110 n. In addition, step 840 occursin real time and continuously while the accessor accesses (and attemptsto access) user account 150. Alternatively, step 840 may occurcontemporaneously for several devices 110 a-110 n, several input/outputdevices 112, and/or several sensors 119. Step 840 receives identity datasets from multiple sources and accessing devices 110 a-110 n and, likestep 810, the data sets may be active or passive information from theaccessor and accessor device.

At step 850, the received identity data is separated into subsets andthe subsets are analyzed by clusters. Much like the steps described inFIG. 7 , the received identity data may first be categorized. The datamay be separated by type of data from a particular accessing device 110,by a type of input/output 112 device, and/or by a sensor 119. The datamay be further separated by active and/or passive acquisitiontechniques. Once separated and categorized, the data subsets may beanalyzed together in clusters. Depending on the received and categorizeddata subsets, the clusters may analyze information from multipleaccessing device 110 sources, from multiple input/output 112 devices,and/or by multiple sensors 119. The cluster analysis itself may befurther separated into tiers. Each individual cluster and/or tier ofclusters may carry a unique weighted multiply factor. Additionally, thereceived data subsets may also carry unique weighted multiply factors aswell. The step 850 analysis will continuously receive and analyze thedata subsets based on pre-established weighted factors andinterconnectivity associations between clusters. Further, the clustersmay be program constructs designed to interact with, or be a part of,identity verification application 140.

At step 860, the analyzed data subsets are outputted by the clusters toa node for final weighing, compilation, and analysis. Much like step850, the node analysis itself may be further separated into tiers. Eachindividual node and/or tier of nodes may analyze output clusters andassign unique weighted multiply factors. The step 860 analysis willcontinuously receive cluster outputs and analyze the outputs based onpre-established weighted factors and interconnectivity associationsbetween the clusters and/or tiers of nodes. Further, the node and/ortiers of nodes may be program constructs designed to interact with, orbe a part of, identity verification application 140.

At step 870, a profile for the accessor is generated based on thereceived data from step 840, cluster analysis from step 850, and nodeweighting from step 860. This accessor profile will be based on theaccessor's accessing behavior. Much like steps 501-505 from FIG. 5 , andstep 820, step 870 generates the profile based on the collected activeand passive data continuously and contemporaneously from step 840. Asdescribed here throughout, the active and passive data will include datacollected from the accessors' accessing device from step 840, such as,username/password; biometric, account, typing pattern, mouse movement,finger pressure rate, or typing error rate data associated with theaccessor; portions of user account 150 previously accessed; theaccessing device 110 static information like type of device, deviceoperating system, device serial number, type of connection; dataobtained from sensors 119 or location based data; as well as, historicaldata stored in memory 114.

The system 100 then compares the initial user profile from step 820 andthe accessor profile from step 870 and determines a likelihood factorthat the two profiles match at step 880. Based on this determinedlikelihood factor, the account provider system 130 and/or identityverification application 140 may determine that the accessor is indeedthe user and authorize the access of user account 150. In oneembodiment, system 100, at step 880, may determine that the initial userprofile and the accessor profile are not identical but are similar.Further, at step 880, the system 100 will determine the likelihood thatthe two profiles are similar within a confidence interval.

At step 890, the system 100 will compare the likelihood factordetermined in step 880 against a pre-determined threshold factor. Ifsystem 100 determines the two profile similarities are not above thepre-determined threshold factor, then, at step 892, the account providersystem 130 and/or identity verification application 140 will alert theuser, or the account provider system 130, of an unsuccessful identityverification attempt. Further, the alert from step 892 may be furtherstored with account provider system 130 for future use and reference.Alternatively, if system 100 determines the two profile similarities areabove the pre-determined threshold factor, then, at step 894, theaccount provider system 100 and/or identity verification application 140will verify the accessor as the user and grant access to user account150. If accessor activity drops the two profile similarities below thepre-determined threshold factor during access of user account 150,permitted access to user account 150 may be terminated, and the system100 may revert back to step 892.

Then at step 896, after either an unsuccessful identity verificationfrom step 892 and/or successful identity verification from step 894,system 100 will modify the initial user profile generated at step 820.Additionally, all the pre-determined values may be altered based on step896 reiterative adjustments. For instance, the pre-determined thresholdfactor from 890 may be raised or lowered based upon repeated likelihoodfactor confidence intervals from step 880. And the weighted factors fromsteps 840-860 may be adjusted based on subsequent determined likelihoodfactors from step 880 and/or step 890 outcomes. Alternatively, weightedfactors from steps 840-860 may be further adjusted from the continuousand contemporaneous received data from step 830 and 840. Additionally,the tiers and cluster sub-categories may be further adjusted as well.

While illustrative embodiments have been described herein, the scopethereof includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations as would be appreciated bythose in the art based on the present disclosure. For example, thenumber and orientation of components shown in the exemplary systems maybe modified. Thus, the foregoing description has been presented forpurposes of illustration only. It is not exhaustive and is not limitingto the precise forms or embodiments disclosed. Modifications andadaptations will be apparent to those skilled in the art fromconsideration of the specification and practice of the disclosedembodiments.

The elements in the claims are to be interpreted broadly based on thelanguage employed in the claims and not limited to examples described inthe present specification or during the prosecution of the application,which examples are to be construed as non-exclusive. It is intended,therefore, that the specification and examples be considered asexemplary only, with a true scope and spirit being indicated by thefollowing claims and their full scope of equivalents.

1-20. (canceled)
 21. A system for verifying a user identify, the systemcomprising: one or more processors programmed with instructions that,when executed by the one or more processors, causes operationscomprising: in connection with a request to access an account of a user,obtaining, from an accessor device of an accessor, verification datacomprising accessor data subsets associated with the accessor; storingthe accessor data subsets in respective clusters, wherein the clustersare nonlinear neural network computational elements; generating clusteranalyses by analyzing the accessor data subsets in the clusters;providing the cluster analyses to node instances to obtain weightedresults, wherein the node instances are nonlinear neural networkcomputation elements based on the clusters; and determining a likelihoodfactor that the accessor is the user based on the weighted results. 22.The system of claim 21, the operations further comprising: generating afirst profile, related to the user, based on information associated withthe account of the user; and generating a second profile, related to theaccessor, based on (i) the verification data obtained from the accessordevice and (ii) the weighted results, wherein determining the likelihoodfactor comprises determining the likelihood factor that the accessor isthe user based on a comparison of the first and second profiles.
 23. Thesystem of claim 22, the operations further comprising: modifying a userprofile associated with the user with data from the second profile basedon a confirmation that the accessor is the user, the confirmation beingbased on the likelihood factor.
 24. The system of claim 22, wherein thecluster analyses are weighted based on a set of rules of the firstprofile to obtain the weighted results.
 25. The system of claim 21,wherein obtaining the verification data comprises obtaining identitydata collected via one or more sensors of the accessor device.
 26. Thesystem of claim 21, wherein obtaining the verification data comprisesobtaining, from the accessor device, (i) accessor data comprising atleast one of username, password, typing pattern, typing cadence, typingerror rate, mouse movement, mouse scrolling rate, mouse clicking rate,mouse clicking error rate, frequency of mouse use, finger pressure,finger error rate, biometrics, or reading rate, and (ii) accessor devicedata comprising at least one of software version, browser type, orconnection speed.
 27. A method comprising: in connection with a requestto access an account of a user, obtaining, from an accessor device of anaccessor, verification data comprising data subsets associated with theaccessor; storing the data subsets in one or more clusters, wherein theone or more clusters are nonlinear neural network computationalelements; generating cluster analyses by analyzing the data subsets inthe one or more clusters; providing the cluster analyses to nodeinstances to obtain weighted results, wherein the node instances arenonlinear neural network computation elements based on the one or moreclusters; and determining a likelihood factor that the accessor is theuser based on the weighted results.
 28. The method of claim 27, furthercomprising: generating a first profile, related to the user, based oninformation associated with the account of the user; and generating asecond profile, related to the accessor, based on (i) the verificationdata obtained from the accessor device and (ii) the weighted results,wherein determining the likelihood factor comprises determining thelikelihood factor that the accessor is the user based on a comparison ofthe first and second profiles.
 29. The method of claim 28, furthercomprising: confirming that that the accessor is the user based on thelikelihood factor exceeding a likelihood threshold.
 30. The method ofclaim 29, further comprising: modifying a user profile associated withthe user with data from the second profile based on the confirmationthat the accessor is the user.
 31. The method of claim 28, wherein thecluster analyses are weighted based on a set of rules of the firstprofile to obtain the weighted results.
 32. The method of claim 27,wherein obtaining the verification data comprises obtaining identitydata collected via one or more sensors of the accessor device.
 33. Themethod of claim 27, wherein obtaining the verification data comprisesobtaining, from the accessor device, (i) accessor data comprising atleast one of username, password, typing pattern, typing cadence, typingerror rate, mouse movement, mouse scrolling rate, mouse clicking rate,mouse clicking error rate, frequency of mouse use, finger pressure,finger error rate, biometrics, or reading rate, and (ii) accessor devicedata comprising at least one of software version, browser type, orconnection speed.
 34. One or more non-transitory computer-readable mediastoring instructions that, when executed by one or more processors,cause operations comprising: in connection with a request to access anaccount of a user, receiving, from an accessor device of an accessor,verification data comprising data subsets associated with the accessor;generating cluster analyses by analyzing the data subsets based on oneor more clusters, wherein the one or more clusters are nonlinear neuralnetwork computational elements; providing the cluster analyses to nodeinstances to obtain weighted results, wherein the node instances arenonlinear neural network computation elements based on the one or moreclusters; and determining a likelihood factor that the accessor is theuser based on the weighted results.
 35. The media of claim 34, theoperations further comprising: generating a first profile, related tothe user, based on information associated with the account of the user;and generating a second profile, related to the accessor, based on (i)the verification data obtained from the accessor device and (ii) theweighted results, wherein determining the likelihood factor comprisesdetermining the likelihood factor that the accessor is the user based ona comparison of the first and second profiles.
 36. The media of claim35, the operations further comprising: confirming that that the accessoris the user based on the likelihood factor exceeding a likelihoodthreshold.
 37. The media of claim 36, the operations further comprising:modifying a user profile associated with the user with data from thesecond profile based on the confirmation that the accessor is the user.38. The media of claim 35, wherein the cluster analyses are weightedbased on a set of rules of the first profile to obtain the weightedresults.
 39. The media of claim 34, wherein obtaining the verificationdata comprises obtaining identity data collected via one or more sensorsof the accessor device.
 40. The media of claim 34, wherein obtaining theverification data comprises obtaining, from the accessor device, (i)accessor data comprising at least one of username, password, typingpattern, typing cadence, typing error rate, mouse movement, mousescrolling rate, mouse clicking rate, mouse clicking error rate,frequency of mouse use, finger pressure, finger error rate, biometrics,or reading rate, and (ii) accessor device data comprising at least oneof software version, browser type, or connection speed.